Domain Knowledge and Provenance in Science Data Systems

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Presented at the AGU Fall Meeting 2009

Concepts:Web Science

Abstract:

Across geosciences, there is a growing drive to make data holdings available to and usable by the general science community. Integral to any data publishing system are ancillary metadata services that provide the necessary information to allow users to find and correctly interpret data holdings. Provenance, metadata that describes the lineage or origin of a data entity, has been recognized by the science community as vital in establishing trust in data holdings and verification of data processing. Provenance search, explanation, and visualization tools are expected to be an important component to any data publishing system. We describe two emerging provenance standards, the Open Provenance Model (OPM) and Proof Markup Language (PML), and show how these standards can be used to capture the lineage of a data product. Further, we introduce the concept of integrating the provenance model with domain models to provide enhanced description of lineage using domain concepts. We describe our approach to model integration, implementation and benefits, and indicate how the domain integrated ontology model assists search, trust, and interpretation of the data.

History

DateCreated ByLink
July 13, 2011
22:50:44
Stephan ZednikDownload

Related Projects:

Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust.
SPCDIS Project LogoSemantic Provenance Capture in Data Ingest Systems (SPCDIS)
Principal Investigator: Peter Fox
Co Investigator: Deborah L. McGuinness
Description: The goal of this project is to develop at the RPI Tetherless World Constellation, based within the NCAR High Altitude Observatory and in collaboration with the University of Texas at El Paso, the University of Michigan and McGuinness Associates a semantically-enabled data ingest capability.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts:
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: ,
Semantic eScience
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.

As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.

Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts:
X-informatics
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: ,